---
title: "AMPs ARGENTINA"
output:
flexdashboard::flex_dashboard:
theme: lumen
source: embed
logo: logo.png
---
```{r setup, include=FALSE}
library(flexdashboard)
library(ggplot2)
library(plotly)
library(spocc)
library(readr)
library(reshape2)
library(leaflet)
library(xts)
library(dygraphs)
library(plotly)
library(lubridate)
library(dplyr)
library(htmltools)
library(DT)
library(shiny)
library(xts)
library(htmlwidgets)
library(vegan)
library(RColorBrewer)
palette(brewer.pal(8, "Set2"))
```
```{r dataread, message=FALSE, warning=FALSE, include=FALSE}
#READ both files metadata and percent cover
# Suppress summaries info
options(dplyr.summarise.inform = FALSE)
#DATA
##Data folder
Data <- "DATA"
#COVER DATA-------------
#We download from CORALNET two files, 1-species cover information and 2- metadata of photoquadrats:
# 1- metadata.csv
# 2- percent_cover.csv
library(readr)
cover <- read_csv(file.path(Data, "percent_covers.csv"))#read cover data
metadata <- read_csv(file.path(Data,"metadata.csv"))#read metadata
AMP_list <- read_csv(file.path(Data,"AMP_list.csv"))
#Merge photoquadrat.metadata and photoquadrat.cover
AMP<- merge(metadata,cover, by.x = "Name", by.y ="Image name", all.x = TRUE)
#Remove original data frames from enviroment
rm(cover)
#rm(metadata)
# Reemplazar "CABO DOS BAHIAS" por "PIMCPA_NORTE" en la columna "locality" de AMP
AMP$locality <- ifelse(AMP$locality == "CABO DOS BAHIAS", "PIMCPA_NORTE", AMP$locality)
# Reemplazar "PIMCPA" por "PIMCPA_SUR" en la columna "locality" de AMP
AMP$locality <- ifelse(AMP$locality == "PIMCPA", "PIMCPA_SUR", AMP$locality)
#all seaweed
AMP_original <- AMP
AMP$algae <- as.numeric(paste(AMP$MAA +AMP$MAEC + AMP$MAEF+ AMP$MAEN+ AMP$MAF+ AMP$MAG+ AMP$MALA+ AMP$MALCB+ AMP$MAS))
#Create long type dataframe
library(reshape)
AMP_long = melt(AMP, id.vars = 1:22, measure.vars = 23:ncol(AMP), variable_name = "CATAMI", value_name ="cover", na.rm = T)
#rename columns because the ontop command is not working
colnames(AMP_long)[23] <- "CATAMI"
colnames(AMP_long)[24] <- "cover"
#Calculate mean, SD, SE for cover data by factors
library(doBy)
Coverdata <- summaryBy(cover ~ CATAMI + strata,data=AMP_long, FUN = function(x) { c(mean = mean(x),SD=sd(x),SE = sqrt(var(x)/length(x)))})
library(doBy)
Coverdata_AMPs <- summaryBy(cover ~ CATAMI + strata +locality ,data=AMP_long, FUN = function(x) { c(mean = mean(x),SD=sd(x),SE = sqrt(var(x)/length(x)))})
library(dplyr)
library(lubridate)
AMP <- AMP %>%
mutate(year = lubridate::year(Date)) %>%
mutate(month = lubridate::month(Date)) %>%
mutate(estacion = ifelse(month %in% c(12, 1, 2, 3, 4, 5), "Cálida", "Fría"))%>%
select(Name,Date, year,month,estacion,everything())
photo_bydate = as.data.frame(table(AMP$year,AMP$site,AMP$locality,AMP$strata))
colnames(photo_bydate)=c("Fecha","Sitio","Localidad","Estrato","n fotocuadrantes")
#SST
#getSST.r was used to get data
## get sampling event dates
samplingDates = unique(AMP$Date)
## read SST values
library(readr)
setwd(paste0(getwd(),"/DATA"))#set new WD to folder DATA
SST = read_csv("ISLAPINGUINO_SST.csv")
SST.clim = read_csv("ISLAPINGUINO_Climatology.csv")
setwd("..")# original WD
library(plotly)
library(dplyr)
library(cols4all)
# Obtener las categorías únicas en la columna 'CATAMI'
categorias_unicas <- unique(Coverdata_AMPs$CATAMI)
num_categorias <- length(categorias_unicas)
# Obtener una paleta de colores de cols4all con 18 colores
paleta_cols4all <- c4a("poly.alphabet", 18)
# Crear un vector de colores asignados a las categorías
colores_categoria <- paleta_cols4all[match(Coverdata_AMPs$CATAMI, categorias_unicas)]
# Asignar los colores al dataframe en una nueva columna llamada 'Color'
Coverdata_AMPs$Color <- colores_categoria
# Obtener las localidades únicas en la columna 'locality'
localidades_unicas <- unique(Coverdata_AMPs$locality)
Coverdata_AMPs$CATAMI
# Definir el dataframe con los nombres actualizados
nombres_actualizados <- recode(Coverdata_AMPs$CATAMI,
"CNCA" = "Anémonas coloniales",
"CNTR" = "Anémonas",
"CRB" = "Cirripedios",
"MOB" = "Bivalvos",
"MOG" = "Gasterópodos",
"SP" = "Esponja",
"WPOT" = "Tubo poliqueto",
"SC" = "Suelo Desnudo",
"Unc" = "Sin definir",
"MAA" = "Algas calcáreas",
"MAEC" = "Algas ramas gruesas",
"MAEF" = "Algas ramas finas",
"MAEN" = "Algas incrustantes",
"MAF" = "Algas filamentosas",
"MAG" = "Algas globosas",
"MALA" = "Algas laminares",
"MALCB" = "Algas formadoras de bosque",
"MAS" = "Algas hojas laminares finas")
# Reemplazar la columna original con los nombres actualizados
Coverdata_AMPs$CATAMI <- nombres_actualizados
```
```{r naa, message=TRUE, warning=TRUE, include=FALSE}
unique(metadata$locality)
```
# Mapa
Column {data-width=600}
-------------------------------------
### Sitios de muestreo
```{r map, message=FALSE, warning=FALSE}
# Combinar las columnas year y estacion en una columna llamada tiempo
AMP$tiempo <- paste(AMP$year, AMP$estacion, sep = "_")
# Crear el mapa
map <- leaflet(options = leafletOptions(zoomControl = TRUE,
zoomSnap = 0.25,
zoomDelta = 1)) %>%
addProviderTiles('Esri.WorldImagery',
options = providerTileOptions(maxNativeZoom=19,maxZoom=100),
group = "Satelite"
) %>%
addProviderTiles(
"OpenStreetMap",
group = "Mapa para zoom"
) %>%
addLayersControl(
baseGroups = c("Satelite", "Mapa para zoom"),
position = "topleft"
)
# Añadir puntos para cada tiempo como capas separadas
for (tiempo in unique(AMP$tiempo)) {
map <- map %>%
addCircleMarkers(
data = AMP[AMP$tiempo == tiempo, ],
~Longitude,
~Latitude,
weight = 0.5,
radius = 4,
fillOpacity = 0.5,
stroke = TRUE,
popup = ~paste("Strata:", strata, "<br>", "Name:", Name),
color = ~factor(strata),
fillColor = ~factor(strata) %>%
{ifelse(. == "HIGHTIDE", "red", ifelse(. == "MIDTIDE", "blue", "green"))},
group = as.character(tiempo) # Asignar cada punto a su tiempo correspondiente como un grupo separado
)
}
# Agregar control de capas para seleccionar el tiempo
map <- map %>%
addLayersControl(
overlayGroups = unique(AMP$tiempo), # Lista de tiempos para seleccionar
options = layersControlOptions(collapsed = FALSE) # Panel desplegado por defecto
) %>%
addControl(html = "<div id='legend'>Estratos: <br><i style='background: red'></i> Alta <br><i style='background: blue'></i> Media <br><i style='background: green'></i> Baja </div>",
position = "bottomright")
map # Mostrar el mapa
```
Column {data-width=400}
-------------------------------------
### Fotoquadrantes
```{r}
library(dplyr)
library(tidyr)
# Calcular la tabla con todos los valores
table_data <- as.data.frame(table(AMP$year, AMP$month, AMP$locality))
# Filtrar la tabla para eliminar filas con n igual a cero
table_filtered <- table_data[table_data$Freq != 0, ]
# Renombrar las columnas
colnames(table_filtered) <- c("Año", "Mes", "Localidad", "n")
# Agregar una columna que clasifique los meses como "Cálida" o "Fría"
table_filtered <- table_filtered %>%
mutate(Estacion = ifelse(Mes %in% c(12, 1, 2, 3, 4, 5), "Cálida", "Fría"))
# Calcular la suma total de la columna n agrupada por Año, Estacion y Localidad
total_n <- table_filtered %>%
group_by(Año, Estacion, Localidad) %>%
summarize(Total_n = sum(n)) %>%
ungroup() %>%
mutate(Total_n = ifelse(is.na(Total_n), 0, Total_n)) # Reemplazar NA con 0 en caso de que no haya datos para una combinación
# Calcular la suma total de la columna n agrupada por Año y Estacion
total_estacion <- total_n %>%
group_by(Año, Estacion) %>%
summarize(Total_n = sum(Total_n)) %>%
ungroup() %>%
mutate(Localidad = "Total") # Agregar "Total" como localidad para estas filas
# Agregar una fila al final con la suma total de la columna n por Año
total_por_año <- total_n %>%
group_by(Año) %>%
summarize(Total_n = sum(Total_n)) %>%
mutate(Estacion = "Total", Localidad = "Total")
# Combinar la tabla total_n con la fila de suma total de estación
tabla_final <- bind_rows(total_n, total_estacion, total_por_año)
# Mostrar la tabla con el total de la columna n agrupada por Año, Estacion y Localidad
knitr::kable(tabla_final, row.names = FALSE)
```
# Cobertura de organismos vivos
```{r taxacover}
# Use this when using percent_covers.csv and full names from CoralNet
# taxacover = AMP_long %>% filter(CATAMI != "Substrate..Consolidated..hard.") %>%
# group_by(site, strata, Image.ID) %>%
# summarise(sumcover = sum(cover, na.rm=T))
# Use this when using percent_covers_AMP2023.cvs and short names from CoralNet
taxacover = AMP_long %>% filter(CATAMI != "SC", CATAMI !="algae") %>%
group_by(locality, strata, Name) %>%
summarise(sumcover = sum(cover, na.rm=T))
pp = ggplot(taxacover, aes(x=factor(strata,level=c('LOWTIDE', 'MIDTIDE', 'HIGHTIDE')), sumcover, fill=strata))
pp = pp + geom_boxplot() + ylab("% Cobertura de todas las especies por cuadrante") + xlab("")+
facet_grid(~locality) +
theme_bw(base_size = 10) + theme(legend.position = "none",axis.text.x = element_text(angle = 90, hjust = 1),strip.text = element_text(size = 5))
ggplotly(pp)
```
```{r eval=FALSE, include=FALSE}
### Coberturas de Molluscos
#<!-- <img src="images/AMP20053.JPG" width="400px"/> -->
# this creates a gauge with % cover values color coded by threshold levels
library(echarts4r)
# Use this when using percent_covers.csv and full names from CoralNet
# value <- Coverdata[Coverdata$CATAMI == "Molluscs..Bivalves", ]
# Use this when using percent_covers_AMP2023.cvs and short names from CoralNet
value <- Coverdata[Coverdata$CATAMI == "MOB", ]
Molluscs <- e_charts() %>%
e_gauge(round(value$cover.mean[1], 0),
"ALTO",
center = c("20%", "20%"),
radius = "35%",
color = "black",
min=0,
max=100,
splitNumber = 5,
axisLine = list(
lineStyle = list(
color=list(
c(1, "green"),
c(1, "green"),
c(1, "green")
)
))) %>%
e_gauge(round(value$cover.mean[3], 0),
"MEDIO",
center = c("50%", "20%"),
radius = "35%",
color = "black",
min=0,
max=100,
splitNumber = 5,
axisLine = list(
lineStyle = list(
color=list(
c(0.20, "red"),
c(0.4, "yellow"),
c(1, "green")
)
))) %>%
e_gauge(round(value$cover.mean[2], 0),
"BAJO",
center = c("80%", "20%"),
radius = "35%",
color = "black",
min=0,
max=100,
splitNumber = 5,
axisLine = list(
lineStyle = list(
color=list(
c(1, "green"),
c(1, "green"),
c(1, "green")
)
))) %>%
e_title("Moluscos (%)")
Molluscs
# # High tide
# gauge(value$cover.mean[1], min = 0, max = 100, symbol = '%',
# gaugeSectors(success = c(60, 100), warning = c(40, 59), danger = c(0, 39)),
# label = "Estrato alto"
# )
#
# # Mid tide
# gauge(value$cover.mean[3], min = 0, max = 100, symbol = '%',
# gaugeSectors(success = c(60, 100), warning = c(40, 59), danger = c(0, 39)),
# label = "Estrato medio"
# )
#
# # Low tide
# gauge(value$cover.mean[2], min = 0, max = 100, symbol = '%',
# gaugeSectors(success = c(60, 100), warning = c(40, 59), danger = c(0, 39)),
# label = "Estrato bajo"
# )
```
```{r eval=FALSE, include=FALSE}
### Coberturas de Macro-Algas
# this creates a gauge with % cover values color coded by threshold levels
library(echarts4r)
# Use short names from CoralNet
value <- Coverdata[Coverdata$CATAMI == "algae", ]
MAS <- e_charts() %>%
e_gauge(round(value$cover.mean[1], 0),
"ALTO",
center = c("20%", "20%"),
radius = "35%",
color = "black",
min=0,
max=100,
splitNumber = 5,
axisLine = list(
lineStyle = list(
color=list(
c(1, "green"),
c(1, "green"),
c(1, "green")
)
))) %>%
e_gauge(round(value$cover.mean[3], 0),
"MEDIO",
center = c("50%", "20%"),
radius = "35%",
color = "black",
min=0,
max=100,
splitNumber = 5,
axisLine = list(
lineStyle = list(
color=list(
c(1, "green"),
c(1, "green"),
c(1, "green")
)
))) %>%
e_gauge(round(value$cover.mean[2], 0),
"BAJO",
center = c("80%", "20%"),
radius = "35%",
color = "black",
min=0,
max=100,
splitNumber = 5,
axisLine = list(
lineStyle = list(
color=list(
c(0.10, "red"),
c(.20, "yellow"),
c(1, "green")
)
))) %>%
e_title("Macro-algas (%)")
MAS
```
# Frecuencia por categorias
```{r}
taxafreq = AMP_long %>% filter(cover>0)%>%
group_by(locality, strata, CATAMI) %>%
summarise(sppfreq = n()) %>% arrange(sppfreq) %>% mutate(sppacum = cumsum(sppfreq))
taxafreq = taxafreq %>% filter(CATAMI !="algae")
pp = ggplot(taxafreq, aes(CATAMI, sppfreq, fill=strata))
pp = pp + geom_bar(stat="identity") + coord_flip() + facet_grid(~locality) +
theme_bw(base_size = 10) + xlab("") + ylab("Número de foto-cuadrantes")+ theme(legend.position = "top",axis.text.x = element_text(angle = 90, hjust = 1),strip.text = element_text(size = 5))
ggplotly(pp)
```
```{r include=FALSE}
library(patchwork)
# Crear una lista para almacenar los gráficos generados en el bucle
plot_list <- list()
# Iniciar un loop para cada localidad
for (localidad in localidades_unicas) {
# Filtrar los datos para la localidad actual
Coverdata_AMPs_site <- filter(Coverdata_AMPs, locality == localidad)
# Crear un gráfico de torta para cada estrato
sel_data.L <- filter(Coverdata_AMPs_site, strata == "LOWTIDE", cover.mean > 0, CATAMI != "algae")
sel_data.M <- filter(Coverdata_AMPs_site, strata == "MIDTIDE", cover.mean > 0, CATAMI != "algae")
sel_data.H <- filter(Coverdata_AMPs_site, strata == "HIGHTIDE", cover.mean > 0, CATAMI != "algae")
p <- plot_ly(labels = ~CATAMI, values = ~cover.mean, legendgroup = ~CATAMI, textinfo = 'label+percent',
marker = list(colors = ~Color)) %>% # Aquí asignamos los colores desde la columna 'Color'
add_pie(data = sel_data.L, name = "Bajo", title = 'Estrato Bajo', domain = list(row = 0, column = 0)) %>%
add_pie(data = sel_data.M, name = "Medio", title = 'Estrato Medio', domain = list(row = 0, column = 1)) %>%
add_pie(data = sel_data.H, name = "Alto", title = 'Estrato Alto', domain = list(row = 0, column = 2)) %>%
layout(title = localidad, showlegend = T,
grid = list(rows = 1, columns = 3),
xaxis = list(showgrid = FALSE, zeroline = FALSE, showticklabels = FALSE),
yaxis = list(showgrid = FALSE, zeroline = FALSE, showticklabels = FALSE))
# Agregar el gráfico a la lista
plot_list[[length(plot_list) + 1]] <- p
}
```
# Cobertura por categorias
Column1 {.tabset .tabset-fade}
-------
### MAR DEL PLATA
```{r}
plot_list[[3]]
```
### ISLOTE LOBOS
```{r}
plot_list[[1]]
```
### PUNTA BS AS
```{r}
plot_list[[6]]
```
### PUNTA LOMA
```{r}
plot_list[[8]]
```
### PUNTA TOMBO
```{r}
plot_list[[9]]
```
### PIMCPA_NORTE
```{r}
plot_list[[4]]
```
### PIMCPA_SUR
```{r}
plot_list[[5]]
```
### PUNTA BUQUE
```{r}
plot_list[[7]]
```
### MAKENKE
```{r}
plot_list[[2]]
```
### MONTE LEON
```{r}
plot_list[[10]]
```
